
import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__lowerCAmelCase     :	List[Any]       			=  {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def a__       (			A_   ):






  '''simple docstring'''





  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts   )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config   )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights   )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if args.student_type == "roberta":
    __magic_name__								=			False
  elif args.student_type == "gpt2":
    __magic_name__								=			False

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if args.student_type == "roberta":
    __magic_name__								=			False

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			argparse.ArgumentParser(description="""Training"""   )
  parser.add_argument("""--force""",	action="""store_true""",	help="""Overwrite dump_path if it already exists."""   )

  parser.add_argument(
      """--dump_path""",	type=A_,	required=A_,	help="""The output directory (log, checkpoints, parameters, etc.)"""   )
  parser.add_argument(
      """--data_file""",	type=A_,	required=A_,	help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""",	)

  parser.add_argument(
      """--student_type""",	type=A_,	choices=["""distilbert""", """roberta""", """gpt2"""],	required=A_,	help="""The student type (DistilBERT, RoBERTa).""",	)
  parser.add_argument("""--student_config""",	type=A_,	required=A_,	help="""Path to the student configuration."""   )
  parser.add_argument(
      """--student_pretrained_weights""",	default=A_,	type=A_,	help="""Load student initialization checkpoint."""   )

  parser.add_argument(
      """--teacher_type""",	choices=["""bert""", """roberta""", """gpt2"""],	required=A_,	help="""Teacher type (BERT, RoBERTa)."""   )
  parser.add_argument("""--teacher_name""",	type=A_,	required=A_,	help="""The teacher model."""   )

  parser.add_argument("""--temperature""",	default=2.0,	type=A_,	help="""Temperature for the softmax temperature."""   )
  parser.add_argument(
      """--alpha_ce""",	default=0.5,	type=A_,	help="""Linear weight for the distillation loss. Must be >=0."""   )
  parser.add_argument(
      """--alpha_mlm""",	default=0.0,	type=A_,	help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""",	)
  parser.add_argument("""--alpha_clm""",	default=0.5,	type=A_,	help="""Linear weight for the CLM loss. Must be >=0."""   )
  parser.add_argument("""--alpha_mse""",	default=0.0,	type=A_,	help="""Linear weight of the MSE loss. Must be >=0."""   )
  parser.add_argument(
      """--alpha_cos""",	default=0.0,	type=A_,	help="""Linear weight of the cosine embedding loss. Must be >=0."""   )

  parser.add_argument(
      """--mlm""",	action="""store_true""",	help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""   )
  parser.add_argument(
      """--mlm_mask_prop""",	default=0.15,	type=A_,	help="""Proportion of tokens for which we need to make a prediction.""",	)
  parser.add_argument("""--word_mask""",	default=0.8,	type=A_,	help="""Proportion of tokens to mask out."""   )
  parser.add_argument("""--word_keep""",	default=0.1,	type=A_,	help="""Proportion of tokens to keep."""   )
  parser.add_argument("""--word_rand""",	default=0.1,	type=A_,	help="""Proportion of tokens to randomly replace."""   )
  parser.add_argument(
      """--mlm_smoothing""",	default=0.7,	type=A_,	help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""",	)
  parser.add_argument("""--token_counts""",	type=A_,	help="""The token counts in the data_file for MLM."""   )

  parser.add_argument(
      """--restrict_ce_to_mask""",	action="""store_true""",	help="""If true, compute the distillation loss only the [MLM] prediction distribution.""",	)
  parser.add_argument(
      """--freeze_pos_embs""",	action="""store_true""",	help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""",	)
  parser.add_argument(
      """--freeze_token_type_embds""",	action="""store_true""",	help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""",	)

  parser.add_argument("""--n_epoch""",	type=A_,	default=3,	help="""Number of pass on the whole dataset."""   )
  parser.add_argument("""--batch_size""",	type=A_,	default=5,	help="""Batch size (for each process)."""   )
  parser.add_argument(
      """--group_by_size""",	action="""store_false""",	help="""If true, group sequences that have similar length into the same batch. Default is true.""",	)

  parser.add_argument(
      """--gradient_accumulation_steps""",	type=A_,	default=50,	help="""Gradient accumulation for larger training batches.""",	)
  parser.add_argument("""--warmup_prop""",	default=0.05,	type=A_,	help="""Linear warmup proportion."""   )
  parser.add_argument("""--weight_decay""",	default=0.0,	type=A_,	help="""Weight decay if we apply some."""   )
  parser.add_argument("""--learning_rate""",	default=5e-4,	type=A_,	help="""The initial learning rate for Adam."""   )
  parser.add_argument("""--adam_epsilon""",	default=1e-6,	type=A_,	help="""Epsilon for Adam optimizer."""   )
  parser.add_argument("""--max_grad_norm""",	default=5.0,	type=A_,	help="""Max gradient norm."""   )
  parser.add_argument("""--initializer_range""",	default=0.02,	type=A_,	help="""Random initialization range."""   )

  parser.add_argument(
      """--fp16""",	action="""store_true""",	help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",	)
  parser.add_argument(
      """--fp16_opt_level""",	type=A_,	default="""O1""",	help=(
          """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      ),	)
  parser.add_argument("""--n_gpu""",	type=A_,	default=1,	help="""Number of GPUs in the node."""   )
  parser.add_argument("""--local_rank""",	type=A_,	default=-1,	help="""Distributed training - Local rank"""   )
  parser.add_argument("""--seed""",	type=A_,	default=56,	help="""Random seed"""   )

  parser.add_argument("""--log_interval""",	type=A_,	default=500,	help="""Tensorboard logging interval."""   )
  parser.add_argument("""--checkpoint_interval""",	type=A_,	default=4000,	help="""Checkpoint interval."""   )
  __magic_name__								=			parser.parse_args()
  sanity_checks(A_   )

  # ARGS #
  init_gpu_params(A_   )
  set_seed(A_   )
  if args.is_master:
    if os.path.exists(args.dump_path   ):
      if not args.force:
        raise ValueError(
            f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
            """ itUse `--force` if you want to overwrite it"""   )
      else:
        shutil.rmtree(args.dump_path   )

    if not os.path.exists(args.dump_path   ):
      os.makedirs(args.dump_path   )
    logger.info(f'''Experiment will be dumped and logged in {args.dump_path}'''   )

    # SAVE PARAMS #
    logger.info(f'''Param: {args}'''   )
    with open(os.path.join(args.dump_path,	"""parameters.json"""   ),	"""w"""   ) as f:
      json.dump(vars(A_   ),	A_,	indent=4   )
    git_log(args.dump_path   )

  __magic_name__	,       __magic_name__	,       __magic_name__								=			MODEL_CLASSES[args.student_type]
  __magic_name__	,       __magic_name__	,       __magic_name__								=			MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  __magic_name__								=			teacher_tokenizer_class.from_pretrained(args.teacher_name   )
  __magic_name__								=			{}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    __magic_name__								=			tokenizer.all_special_tokens.index(A_   )
    __magic_name__								=			tokenizer.all_special_ids[idx]
  logger.info(f'''Special tokens {special_tok_ids}'''   )
  __magic_name__								=			special_tok_ids
  __magic_name__								=			tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'''Loading data from {args.data_file}'''   )
  with open(args.data_file,	"""rb"""   ) as fp:
    __magic_name__								=			pickle.load(A_   )

  if args.mlm:
    logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''   )
    with open(args.token_counts,	"""rb"""   ) as fp:
      __magic_name__								=			pickle.load(A_   )

    __magic_name__								=			np.maximum(A_,	1   ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      __magic_name__								=			0.0  # do not predict special tokens
    __magic_name__								=			torch.from_numpy(A_   )
  else:
    __magic_name__								=			None

  __magic_name__								=			LmSeqsDataset(params=A_,	data=A_   )
  logger.info("""Data loader created."""   )

  # STUDENT #
  logger.info(f'''Loading student config from {args.student_config}'''   )
  __magic_name__								=			student_config_class.from_pretrained(args.student_config   )
  __magic_name__								=			True

  if args.student_pretrained_weights is not None:
    logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''   )
    __magic_name__								=			student_model_class.from_pretrained(args.student_pretrained_weights,	config=A_   )
  else:
    __magic_name__								=			student_model_class(A_   )

  if args.n_gpu > 0:
    student.to(f'''cuda:{args.local_rank}'''   )
  logger.info("""Student loaded."""   )

  # TEACHER #
  __magic_name__								=			teacher_model_class.from_pretrained(args.teacher_name,	output_hidden_states=A_   )
  if args.n_gpu > 0:
    teacher.to(f'''cuda:{args.local_rank}'''   )
  logger.info(f'''Teacher loaded from {args.teacher_name}.'''   )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(A_,	A_   )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(A_,	A_   )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0   ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  __magic_name__								=			Distiller(
      params=A_,	dataset=A_,	token_probs=A_,	student=A_,	teacher=A_   )
  distiller.train()
  logger.info("""Let's go get some drinks."""   )


if __name__ == "__main__":
    main()



from __future__ import annotations

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if b == 0:
    return (1, 0)
  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	a % b   )
  __magic_name__								=			a // b
  return (y, x - k * y)

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m

def a__       (			A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  if b < 0:
    __magic_name__								=			(b % n + n) % n
  return b

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			invert_modulo(A_,	A_   ), invert_modulo(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='chinese_remainder_theorem', verbose=True)
    testmod(name='chinese_remainder_theorem2', verbose=True)
    testmod(name='invert_modulo', verbose=True)
    testmod(name='extended_euclid', verbose=True)

